Got myself a few months ago into the optimization rabbit hole as I had a slow quant finance library to take care of, and for now my most successful optimizations are using local memory allocators (see my C++ post, I also played with mimalloc which helped but custom local memory allocators are even better) and rethinking class layouts in a more “data-oriented” way (mostly going from array-of-structs to struct-of-arrays layouts whenever it’s more advantageous to do so, see for example this talk).
What are some of your preferred optimizations that yielded sizeable gains in speed and/or memory usage? I realize that many optimizations aren’t necessarily specific to any given language so I’m asking in !programming@programming.dev.
Lemmy is probably a good live example of how sometimes going for a “faster language” like Rust isn’t going to magically make a bad SQL database design better or slow queries faster: https://github.com/LemmyNet/lemmy/issues/2877
So yeah, SQL optimization stories are definitely welcome too!
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Lemmy is probably a good live example of how sometimes going for a “faster language” like Rust isn’t going to magically make a bad SQL database design better or slow queries faster: https://github.com/LemmyNet/lemmy/issues/2877
So yeah, SQL optimization stories are definitely welcome too!
That was a good read. Thanks for the link.
I read through most of this and it was very informative. Thank you very much.